Sorry, but I don't think AI is entirely to blame here. When I graduated from a CS program at a top-10 school, I felt frustrated that the professors didn't ever teach. They had slides. They read off slides, verbatim. They explained things sometimes if you asked them, but most often in a very elitist and condescending tone. Like in the movie Good Will Hunting, you could have learned nearly all of it and more by borrowing those books for free from the library. Or, just opening a complex OSS project and learning to contribute.
And quite honestly. It shows in the CS grad population too. A lot of us are condescending toward anything that doesn't make sense to us. But, I digress.
The best engineers I've worked with are all non traditional backgrounds, non degree or degree holders from non elite schools. They think differently, they tinker, they are incredibly nice and patient, and do it for the love of connecting humans to technology.
Look up the names mentioned in the article. Garcia, Ranade, Nelson. All of them are involved with highly theoretical mathematics and scientific computing. Just because you're good at 1 thing does not mean you are qualified to teach. And none of these professors are trained or taught or graded or performance managed on how they teach. For most of them, its just required that they spend 10% of their time in the classroom lecturing.
Let's be honest about another thing. 99% of EECS graduates, even from elite schools, are wrangling objects and their relationships to a graph. Simply put, we're all just a bunch of glorified JSON massage therapists. It just so happens that we get paid well for it, and we hold that over people. The same happens in the classroom.
I think in order to facilitate a healthy, educational environment for young adults, we as adults must encourage, motivate and make that environment fun and practical. We force feed binary trees and the compiler AST's, but we need to make it fun. It's like the commonly accepted saying: Schools kill creativity :(.
What a terribly ambiguous title. "Failing grades soar after xyz" makes it sound like xyz has helped what were previously terrible, failing grades become good ones.
> Some of the numbers that you saw from the number of students who receive failing grades were because we caught them (cheating) and prosecuted them and are sending their cases to the center for student conduct,” Garcia said. According to Garcia, nearly 30 students in CS 10 were caught cheating on take-home exams in spring 2026.
I believe it’s still a single section, so probably around 250 (at least that’s about what it was when I was there a long time ago). Compared to the 1000+ who take 61A.
And if cheating was triggered using AI detectors, was it real?
AI detectors are pretty mid in practice - they tend to have a lot of false positives for "B" students who are okay, but can still be struggled to be more coherent than AIs are. There are some specific triggers that AIs are way more likely to do than students, but a lot of AI detectors will trigger on this "almost there, but you're still struggling" level of essay writing that might get a B, B-.
I could expect the same might be true for CS students even though I haven't seen how AI detectors work for CS/math homework.
“I’m a strong, strong opponent of what Harvard is doing to say that only a fraction of students can earn A’s,” Garcia said. “I think you should have clear standards for what an A means, and then give tons of opportunity for people … to get to that A bar without lowering the standard. So everybody who’s curving is hiding that effect. It’s completely hiding that effect, and it’s pretending as if nothing’s wrong, and something is definitely wrong.”
To do this, you have to be a professor who has a strong idea of what subject mastery looks like. Not available to most.
In a broad sense, this distinction between Harvard and Cal is the distinction between an old money Ivy and a flagship state school. One exists to propagate a social hierarchy, and the other aims to allow all entrants to succeed.
Ironically, the techniques of the latter yield the results of the first, but everybody gets to keep a pure heart.
I'm confused by Garcia's statement as well because CS@Cal traditionally uses a bell curve which is even stricter than Harvard's changes, because Harvard doesn't have the same stringent GPA requirements to declare a concentration unlike declaring an impacted major at L&S Cal.
Anyone with a pulse can declare a CS concentration at Harvard and muddle by (you actually need to try in order to get a C/C-). Of course, GPAs are calculated differently at Harvard compared to other universities, as a B- is treated at a 2.67 but most other programs treat that as a C+.
Grades only matter as much as being able to transfer just to the real world.
People can use AI to outsource their learning, but if they use ai to outsource their understanding they just set themselves up to fail even more.
From what I’ve seen, how students are using ai (not that they are using ai) is making them less prepared for the real world, which unfortunately is changing faster than ever at the same time to create double impact.
"According to Berkeleytime, 35.3% of CS 10 students and 10.6% of CS 61A students received F’s in spring 2026"
Alternatively, more students are taking CS10 and CS61A irrespective of aptitude.
Anyone can code, but not everyone can become an employable SWE.
Anyone who has first or second hand experience with Cal or any other university knows how impacted CS majors have become, and how everyone is attempting to become a CS major because it's the easiest path to multiple high paying white collar careers.
And in all honesty, it's not like CS@Cal never had weedout classes (I remember CS70, CS61B, and Math54 had reputations of being the L&S weedout classes).
The question comes sooner than the students being tested on the job market. Another possibility is that dropping standardized testing was a net bad idea.
At UC Berkeley L&S, students are undeclared by default, and everyone is incentivized to take the intro CS classes (CS10, CS61A) irrespective of aptitude because worst case they can declare a CS minor or use the classes for other adjacent degrees (eg. Applied Math, Data Science).
Additionally, while Cal doesn't require standardized tests, most students who applied and attended already took the SAT, ACT, and APs becuase they cross-applied to other universities as well. This is reflected in UC Berkeley's HS Weighted GPA being in the 4.31-4.65 range [0], which means most students will have taken at least 6 AP classes.
Hell, I attended an Ivy and even then Cal was a target program for me, as well as my peers. If I didn't get into my Ivy I would have ended up at Cal and ended up in the same position.
Spring 2026 saw a marked shift in student performance. We saw it in intro physics courses on the East coast too. I bet anyone who cared to look saw it.
I'm not denying that. I'm just wondering if anyone measured if there is a correlation effect being induced by CS major declaration requirements.
Barely over a decade ago, CS tended to be a large but not too large major by enrollment in most universities yet nowadays it is the most in-demand major in most universities. You can see this at Stanford [0], but most other programs as well.
I read something interesting yesterday on the subject of AI in education (though, it has consequences to broader society too):
The goal of education is to impart knowledge in the student, preferably correct knowledge. The goal of an LLM is to produce an output that is convincingly human. It's not even that they're opposed, as much as they're ships for whom Polaris is in a completely different direction.
"Hallucinations" as they're called, or more plainly stated when the machine makes some shit up, are perfectly understandable in this context, as are the struggles of every single AI firm to get rid of them. Namely: the machine is functioning exactly as it is designed to, so how can you possibly fix it? It's working. The goal of an LLM is to produce text that passes for human, and apart from the obvious LLM tells, it largely does. Like say what you will about their lack of intelligence, the writing is solid. It's grammatically correct, spelling is dead on, what have you.
It reminds me of the famous phrase from Chomsky: Colorless green ideas sleep furiously. A sentence which is perfectly grammatically valid but is also completely devoid of meaning. An LLM would write that sentence, and it would be working correctly.
All of that to say: for all the things they CAN do and CAN be used for, I think we have to draw a hard line at education. I just don't think AI has a place in it. Of course that presumes that the goal of education is to, well, educate people, and especially here in the States but also abroad, we have been putting other interests, especially capital, far ahead of that for decades. I expect no different here.
And before someone comes in to go "WELL HOW DO YOU THINK YOU'RE GONNA STOP IT LUDDITE IT'S THE FUTUUUUUURE" yes, I'm sure as long as these exist and are available to people tech literate enough to access and use them, whatever that means into the far flung future, they will be a factor. Just like cheating, just like plagiarism, just like everything else that will get you kicked out of school. And the answer is the same: it will be stopped by institutions, imperfectly, and it will also happen anyway and with the same consequence: those responsible will mostly be harming themselves for short-term gains.
Respectfully, I disagree. I think there's absolutely a case for AI being encouraged in younger people, and there's room for these tools. I've been leaning on LLMs for side learning in side projects, and it has concretely helped me with conceptual questions about math and Vulkan as I've been trying to learn some graphics basics with side projects.
I would grant: I was not the most studious kid, I could definitely stand to learn how to read code a lot more effectively than I do; but I have found being able to ask a computer, "what portions of the Vulkan Programming Guide are less relevant with Vulkan's design changes since the release" pointing me to the dynamic rendering extensions and placing it into context, with inline code and links out to useful blog posts for additional reading, that sort of thing is very helpful.
Working on a prototype before I was trying to learn Vulkan, I was using it to explore SDL_GPU's API which definitely had some gaps in its documentation. Granted again, I could have referenced the sample code - I am sure you'll prefer I'd have done that - but it helped to get information about what each piece of the API was doing, and gave reasonable results that made sense and did inform me enough to understand what I was doing, turning much of that into an interactive learning of basic GPU programming for graphics. Where the AI hallucinated, it was often on things like method names, which I was able to read through and find the methods it was intending to name. (This only occurred once or twice when I was learning).
Unrelated, but adding the C macro syntax and nesting macros, which I could have an LLM explain inline and link the GNU manual. Never got that taught to me in a C course. Man, computers are complicated!
These have not replaced textbooks; I have been using them alongside textbooks and handwriting code for practice, and they work as a very good complement. I also sometimes use them to unblock me - I don't know CMake very well and lean on AI to do CMake, so I can focus on learning C++ and graphics, which is my primary objective right now.
I would add too, I have for fun given it prompts about various topics I learned in university, and I often will get answers that are bang-on what I learned in university undergraduate courses - the topics I tried were welfare state taxonomies, distributed systems, disk storage performance, filesystem layouts and internals.
Boy, this would've been cool for me as a kid. There's just so much information right there, and pointing you to topics and textbooks a couple questions away, I wish I had these tools. I was a curious kid in a terrible MAGA-esque family that was deeply uncurious about the world, had no knowledge of any advanced subject and basically mocked me for trying to learn more about stuff. And you go to the school library and it's all kids shit, not even an option to try and reach out for more. Now smart kids might be able to go just learn shit very freely and be pointed to textbooks, and go pirate them off some Russian site, and start learning and go tutor themselves, as I'm doing today as an adult.
At least knowing myself and knowing if there's another kid like me, I think they would deeply enjoy having a natural language encyclopedia, if we can get it as close to that as possible. I think even with some error inherent, if the tools can be often and directionally correct, that would be a plus. I went to university, and the professors there hallucinated some things so embarrassing it should bar them from teaching, for the standards people hold LLMs to! i.e., sanitizing conspiracy theories that Android records all language through the microphone therefore iOS is better, Apple Silicon is more battery efficient because it is RISC and not CISC. Got a terrible history of computer graphics technology you'd know was slanted if you watch the 8 Bit Guy on YouTube. Rubbish.
The thing that worries me, and what this article really talks about, are the kids that just don't give a shit. They are not new - when I went to high school, before AI, stupid kids would copy code off the internet. I think AI probably makes it worse because it makes it harder to call out and enforce against it, and agreed, that should be stopped. But to me, that is mainly a cultural problem. Too many Americans are completely uncurious and just spout garbage; there are a lot of kids who grow up in that cesspool and are going to grow up uncurious, and then AI acts as a shortcut rather than a vehicle of curiosity.
And granted, maybe AI is less useful when you are in a structured environment - but the structured environment has its downsides. Even in that environment many of the TAs were clueless and unhelpful, or just too damn busy or already too knowledgeable to meet students where they were at. Again, talk about hallucinations with TAs! Many times in my experience. And that's all to say nothing about getting people to not just do homework but actually go get curious about things and try stuff that isn't required of them.
I think there will be some culture that remains curious, and has these tools, will come to grips with where they can help, where they go wrong, how to balance it with other learning methods; and I think they are going to have kids that absorb a lot more knowledge and get to play with topics and learn things, faster, to each kids' interest, perhaps even individualized tutoring at better scale - I hope that is possible.
I hope the United States as well, but maybe not, because holy cow our culture and attitudes are plainly terrible these days. Your comment is pretty representative of how most people react if I suggest this or talk about my own experiences I'm describing here. But I hope at least I'm arguing something comprehensive here. There is too little conversation beyond hyperbolic nonsense on the internet; I consider "FUTURE LUDDITE" etc. to be in that realm.
I will add, too, although less relevant to education than just generally - for all the talk that these tools must be useless and incorrect, that just plainly does not map to my experience using these tools. AI can chew through a debug log on a custom system and pick out root causes on behaviors very effectively, in my experience.
It is just hard to reconcile that denigration of AI with the typical experience I have using these tools in the real world. It is not omnipotent or God, but it can effectively assist in work. There is a certain cognitive dissonance I feel when I walk away from using the tool to help accomplish particular tasks, then hear over and over people say the technology is fundamentally useless and fundamentally does not work. I guess I am just not enough of an academic to understand how something can accomplish work yet fundamentally isn't, somehow.
Sorry, but I don't think AI is entirely to blame here. When I graduated from a CS program at a top-10 school, I felt frustrated that the professors didn't ever teach. They had slides. They read off slides, verbatim. They explained things sometimes if you asked them, but most often in a very elitist and condescending tone. Like in the movie Good Will Hunting, you could have learned nearly all of it and more by borrowing those books for free from the library. Or, just opening a complex OSS project and learning to contribute.
And quite honestly. It shows in the CS grad population too. A lot of us are condescending toward anything that doesn't make sense to us. But, I digress.
The best engineers I've worked with are all non traditional backgrounds, non degree or degree holders from non elite schools. They think differently, they tinker, they are incredibly nice and patient, and do it for the love of connecting humans to technology.
Look up the names mentioned in the article. Garcia, Ranade, Nelson. All of them are involved with highly theoretical mathematics and scientific computing. Just because you're good at 1 thing does not mean you are qualified to teach. And none of these professors are trained or taught or graded or performance managed on how they teach. For most of them, its just required that they spend 10% of their time in the classroom lecturing.
Let's be honest about another thing. 99% of EECS graduates, even from elite schools, are wrangling objects and their relationships to a graph. Simply put, we're all just a bunch of glorified JSON massage therapists. It just so happens that we get paid well for it, and we hold that over people. The same happens in the classroom.
I think in order to facilitate a healthy, educational environment for young adults, we as adults must encourage, motivate and make that environment fun and practical. We force feed binary trees and the compiler AST's, but we need to make it fun. It's like the commonly accepted saying: Schools kill creativity :(.
What a terribly ambiguous title. "Failing grades soar after xyz" makes it sound like xyz has helped what were previously terrible, failing grades become good ones.
Failing news headlines soar after AI takes over reporters jobs.
*quietly takes over reporters’ jobs
Precise.
At this point I would support a ban on generative AI by anyone under 18, or even perhaps 21 years of age.
A bunch of science fiction stories had "first connection to cyberspace" as a coming of age event, maybe those authors were on to something.
It’s like the greatest teacher. Plus it’s not toxic like social media. Banning it would be a shame.
Its not teaching. These people cant pass a the class. They never went through the friction needed to learn
> Some of the numbers that you saw from the number of students who receive failing grades were because we caught them (cheating) and prosecuted them and are sending their cases to the center for student conduct,” Garcia said. According to Garcia, nearly 30 students in CS 10 were caught cheating on take-home exams in spring 2026.
Anybody know how many students take CS 10 in a typical spring semester?
I believe it’s still a single section, so probably around 250 (at least that’s about what it was when I was there a long time ago). Compared to the 1000+ who take 61A.
Or how many are normally caught cheating?
Did they use AI to detect AI using cheaters?
And if cheating was triggered using AI detectors, was it real?
AI detectors are pretty mid in practice - they tend to have a lot of false positives for "B" students who are okay, but can still be struggled to be more coherent than AIs are. There are some specific triggers that AIs are way more likely to do than students, but a lot of AI detectors will trigger on this "almost there, but you're still struggling" level of essay writing that might get a B, B-.
I could expect the same might be true for CS students even though I haven't seen how AI detectors work for CS/math homework.
“I’m a strong, strong opponent of what Harvard is doing to say that only a fraction of students can earn A’s,” Garcia said. “I think you should have clear standards for what an A means, and then give tons of opportunity for people … to get to that A bar without lowering the standard. So everybody who’s curving is hiding that effect. It’s completely hiding that effect, and it’s pretending as if nothing’s wrong, and something is definitely wrong.”
To do this, you have to be a professor who has a strong idea of what subject mastery looks like. Not available to most.
But ... It is exactly the right idea IMO
In a broad sense, this distinction between Harvard and Cal is the distinction between an old money Ivy and a flagship state school. One exists to propagate a social hierarchy, and the other aims to allow all entrants to succeed.
Ironically, the techniques of the latter yield the results of the first, but everybody gets to keep a pure heart.
I'm confused by Garcia's statement as well because CS@Cal traditionally uses a bell curve which is even stricter than Harvard's changes, because Harvard doesn't have the same stringent GPA requirements to declare a concentration unlike declaring an impacted major at L&S Cal.
Anyone with a pulse can declare a CS concentration at Harvard and muddle by (you actually need to try in order to get a C/C-). Of course, GPAs are calculated differently at Harvard compared to other universities, as a B- is treated at a 2.67 but most other programs treat that as a C+.
Grades only matter as much as being able to transfer just to the real world.
People can use AI to outsource their learning, but if they use ai to outsource their understanding they just set themselves up to fail even more.
From what I’ve seen, how students are using ai (not that they are using ai) is making them less prepared for the real world, which unfortunately is changing faster than ever at the same time to create double impact.
I had dwindling math skills way before AI made it cool.
"According to Berkeleytime, 35.3% of CS 10 students and 10.6% of CS 61A students received F’s in spring 2026"
Alternatively, more students are taking CS10 and CS61A irrespective of aptitude.
Anyone can code, but not everyone can become an employable SWE.
Anyone who has first or second hand experience with Cal or any other university knows how impacted CS majors have become, and how everyone is attempting to become a CS major because it's the easiest path to multiple high paying white collar careers.
And in all honesty, it's not like CS@Cal never had weedout classes (I remember CS70, CS61B, and Math54 had reputations of being the L&S weedout classes).
The question comes sooner than the students being tested on the job market. Another possibility is that dropping standardized testing was a net bad idea.
This is orthogonal to standardized testing.
At UC Berkeley L&S, students are undeclared by default, and everyone is incentivized to take the intro CS classes (CS10, CS61A) irrespective of aptitude because worst case they can declare a CS minor or use the classes for other adjacent degrees (eg. Applied Math, Data Science).
Additionally, while Cal doesn't require standardized tests, most students who applied and attended already took the SAT, ACT, and APs becuase they cross-applied to other universities as well. This is reflected in UC Berkeley's HS Weighted GPA being in the 4.31-4.65 range [0], which means most students will have taken at least 6 AP classes.
Hell, I attended an Ivy and even then Cal was a target program for me, as well as my peers. If I didn't get into my Ivy I would have ended up at Cal and ended up in the same position.
[0] - https://admissions.berkeley.edu/apply-to-berkeley/student-pr...
CS is now in the College of Computing, Data Science, and Society instead of the College of Letters & Science
Huh. TIL Cal started a College of Computing in 2023 with direct admissions into CS.
Spring 2026 saw a marked shift in student performance. We saw it in intro physics courses on the East coast too. I bet anyone who cared to look saw it.
I'm not denying that. I'm just wondering if anyone measured if there is a correlation effect being induced by CS major declaration requirements.
Barely over a decade ago, CS tended to be a large but not too large major by enrollment in most universities yet nowadays it is the most in-demand major in most universities. You can see this at Stanford [0], but most other programs as well.
[0] - https://stanforddaily.com/2020/04/25/stanford-in-the-2010s-t...
I read something interesting yesterday on the subject of AI in education (though, it has consequences to broader society too):
The goal of education is to impart knowledge in the student, preferably correct knowledge. The goal of an LLM is to produce an output that is convincingly human. It's not even that they're opposed, as much as they're ships for whom Polaris is in a completely different direction.
"Hallucinations" as they're called, or more plainly stated when the machine makes some shit up, are perfectly understandable in this context, as are the struggles of every single AI firm to get rid of them. Namely: the machine is functioning exactly as it is designed to, so how can you possibly fix it? It's working. The goal of an LLM is to produce text that passes for human, and apart from the obvious LLM tells, it largely does. Like say what you will about their lack of intelligence, the writing is solid. It's grammatically correct, spelling is dead on, what have you.
It reminds me of the famous phrase from Chomsky: Colorless green ideas sleep furiously. A sentence which is perfectly grammatically valid but is also completely devoid of meaning. An LLM would write that sentence, and it would be working correctly.
All of that to say: for all the things they CAN do and CAN be used for, I think we have to draw a hard line at education. I just don't think AI has a place in it. Of course that presumes that the goal of education is to, well, educate people, and especially here in the States but also abroad, we have been putting other interests, especially capital, far ahead of that for decades. I expect no different here.
And before someone comes in to go "WELL HOW DO YOU THINK YOU'RE GONNA STOP IT LUDDITE IT'S THE FUTUUUUUURE" yes, I'm sure as long as these exist and are available to people tech literate enough to access and use them, whatever that means into the far flung future, they will be a factor. Just like cheating, just like plagiarism, just like everything else that will get you kicked out of school. And the answer is the same: it will be stopped by institutions, imperfectly, and it will also happen anyway and with the same consequence: those responsible will mostly be harming themselves for short-term gains.
Respectfully, I disagree. I think there's absolutely a case for AI being encouraged in younger people, and there's room for these tools. I've been leaning on LLMs for side learning in side projects, and it has concretely helped me with conceptual questions about math and Vulkan as I've been trying to learn some graphics basics with side projects.
I would grant: I was not the most studious kid, I could definitely stand to learn how to read code a lot more effectively than I do; but I have found being able to ask a computer, "what portions of the Vulkan Programming Guide are less relevant with Vulkan's design changes since the release" pointing me to the dynamic rendering extensions and placing it into context, with inline code and links out to useful blog posts for additional reading, that sort of thing is very helpful.
Working on a prototype before I was trying to learn Vulkan, I was using it to explore SDL_GPU's API which definitely had some gaps in its documentation. Granted again, I could have referenced the sample code - I am sure you'll prefer I'd have done that - but it helped to get information about what each piece of the API was doing, and gave reasonable results that made sense and did inform me enough to understand what I was doing, turning much of that into an interactive learning of basic GPU programming for graphics. Where the AI hallucinated, it was often on things like method names, which I was able to read through and find the methods it was intending to name. (This only occurred once or twice when I was learning).
Unrelated, but adding the C macro syntax and nesting macros, which I could have an LLM explain inline and link the GNU manual. Never got that taught to me in a C course. Man, computers are complicated!
These have not replaced textbooks; I have been using them alongside textbooks and handwriting code for practice, and they work as a very good complement. I also sometimes use them to unblock me - I don't know CMake very well and lean on AI to do CMake, so I can focus on learning C++ and graphics, which is my primary objective right now.
I would add too, I have for fun given it prompts about various topics I learned in university, and I often will get answers that are bang-on what I learned in university undergraduate courses - the topics I tried were welfare state taxonomies, distributed systems, disk storage performance, filesystem layouts and internals.
Boy, this would've been cool for me as a kid. There's just so much information right there, and pointing you to topics and textbooks a couple questions away, I wish I had these tools. I was a curious kid in a terrible MAGA-esque family that was deeply uncurious about the world, had no knowledge of any advanced subject and basically mocked me for trying to learn more about stuff. And you go to the school library and it's all kids shit, not even an option to try and reach out for more. Now smart kids might be able to go just learn shit very freely and be pointed to textbooks, and go pirate them off some Russian site, and start learning and go tutor themselves, as I'm doing today as an adult.
At least knowing myself and knowing if there's another kid like me, I think they would deeply enjoy having a natural language encyclopedia, if we can get it as close to that as possible. I think even with some error inherent, if the tools can be often and directionally correct, that would be a plus. I went to university, and the professors there hallucinated some things so embarrassing it should bar them from teaching, for the standards people hold LLMs to! i.e., sanitizing conspiracy theories that Android records all language through the microphone therefore iOS is better, Apple Silicon is more battery efficient because it is RISC and not CISC. Got a terrible history of computer graphics technology you'd know was slanted if you watch the 8 Bit Guy on YouTube. Rubbish.
The thing that worries me, and what this article really talks about, are the kids that just don't give a shit. They are not new - when I went to high school, before AI, stupid kids would copy code off the internet. I think AI probably makes it worse because it makes it harder to call out and enforce against it, and agreed, that should be stopped. But to me, that is mainly a cultural problem. Too many Americans are completely uncurious and just spout garbage; there are a lot of kids who grow up in that cesspool and are going to grow up uncurious, and then AI acts as a shortcut rather than a vehicle of curiosity.
And granted, maybe AI is less useful when you are in a structured environment - but the structured environment has its downsides. Even in that environment many of the TAs were clueless and unhelpful, or just too damn busy or already too knowledgeable to meet students where they were at. Again, talk about hallucinations with TAs! Many times in my experience. And that's all to say nothing about getting people to not just do homework but actually go get curious about things and try stuff that isn't required of them.
I think there will be some culture that remains curious, and has these tools, will come to grips with where they can help, where they go wrong, how to balance it with other learning methods; and I think they are going to have kids that absorb a lot more knowledge and get to play with topics and learn things, faster, to each kids' interest, perhaps even individualized tutoring at better scale - I hope that is possible.
I hope the United States as well, but maybe not, because holy cow our culture and attitudes are plainly terrible these days. Your comment is pretty representative of how most people react if I suggest this or talk about my own experiences I'm describing here. But I hope at least I'm arguing something comprehensive here. There is too little conversation beyond hyperbolic nonsense on the internet; I consider "FUTURE LUDDITE" etc. to be in that realm.
I will add, too, although less relevant to education than just generally - for all the talk that these tools must be useless and incorrect, that just plainly does not map to my experience using these tools. AI can chew through a debug log on a custom system and pick out root causes on behaviors very effectively, in my experience.
It is just hard to reconcile that denigration of AI with the typical experience I have using these tools in the real world. It is not omnipotent or God, but it can effectively assist in work. There is a certain cognitive dissonance I feel when I walk away from using the tool to help accomplish particular tasks, then hear over and over people say the technology is fundamentally useless and fundamentally does not work. I guess I am just not enough of an academic to understand how something can accomplish work yet fundamentally isn't, somehow.
Some people need Jesus, but y'all need Kant ;)
"Enlightenment is man's emergence from his self-imposed nonage. Nonage is the inability to use one's own understanding without another's guidance."
https://www.columbia.edu/acis/ets/CCREAD/etscc/kant.html